Computational Biology of Infection Research
The research of the group focuses on the data-driven analysis of biological questions from infection research, as well as method development to solve prediction problems for large biological data sets.
Cancer refers to a class of diseases in which uncontrolled cell division in a specific tissue causes the growth of tumors or even the spread of metastases to other locations in the body. According to the world health organization (WHO), cancer will be the most common cause of death worldwide in 2010. The trait affects people of all ages, but is highly associated to environmental factors such as tobacco consumation, diet/obesity, exposure to radiation or polution, stress and the overall lifestyle.
The genetic factors manifest in two classes of genes: oncogenes promote tumor growth by allowing hyperactive cell growth/division or protecting against programmed cell death, and tumor surpressor genes, which tend to be inactivated in cancer cells. Since a successful treatment highly depends on a diagnosis in an early stage of the disease, methods to estimate and predict the disease risk are needed. In particular, a better understanding of the mechanisms that activate, inactivate or regulate oncogenes and tumor surpressor genes is of outmost importance, and with the recent development of next generation high-throughput sequencing technologies improved classification and personalized therapy becomes more and more feasible.
MicroRNAs (miRNAs) are non-coding RNAs about 22 nucleotides in length that regulate a variety of genes. In recent years, strong evidence has been collected that miRNAs play an important role in cancer development and that miRNA expression profiles can be used to classify and rate human cancers. In collaboration with the Cancer Genomics group at the Max-Planck-Institute for Molecular Genetics, we applied statistical modelling techniques to search for known and yet unknown miRNA candidates potentially associated with three different development states of colon cancer.
G.A. Calin, C.M.
CroceMicroRNA signatures in human cancers
Nature Reviews Cancer 2006, 6(11): pp. 857-66
T.A. Farazi, M. Brown, P. Morozov, J.J. Ten Hoeve, I.Z. Ben-Dov, V. Hovestadt, M. Hafner, N. Renwick, A. Mihailović, L.F. Wessels, T. Tuschl
Bioinformatic analysis of barcoded cDNA libraries for small RNA profiling by next generation sequencing
Methods 2012, 58(2): pp. 171 - 87
M. Hafner, M. Landthaler, L. Burger, M. Khorshid, J. Hausser, P. Berninger, A. Rothballer, M. Ascano, A.C. Jungkamp, M. Munschauer, A. Ulrich, G.S. Wardle, S. Dewell, M. Zavolan, T. Tuschl
Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP
Cell 2010, 141(1): pp. 129 - 141
- Christina Kratsch nèe Tusche
- Philipp Münch
- Linda Klesper
Present and former collaborators
Arndt Borkhardt, Jessica Spitzer, Ute Fischer, Department of Pediatric Oncology, Haematology and Immunology, Heinrich-Heine University, Düsseldorf, Germany
Michal-Ruth Schweiger, Christina Röhr, Martin Kerrick and Stefan Börno, Cancer-Genomics-Gruppe, Max-Planck-Institut für Molekulare Genetik, Berlin, Germany
Prof Dr Alice McHardy
Head of department Computational Biology of Infection Research
+49 531 391-55271